Quark Mass Models and Reinforcement Learning
Sign Up to like & getrecommendations! Published in 2021 at "Journal of High Energy Physics"
DOI: 10.1007/jhep08(2021)161
Abstract: Abstract In this paper, we apply reinforcement learning to the problem of constructing models in particle physics. As an example environment, we use the space of Froggatt-Nielsen type models for quark masses. Using a basic… read more here.
Keywords: mass models; physics; reinforcement learning; models reinforcement ... See more keywords
Shot noise in next-generation neural mass models for finite-size networks.
Sign Up to like & getrecommendations! Published in 2022 at "Physical review. E"
DOI: 10.1103/physreve.106.l062302
Abstract: Neural mass models is a general name for various models describing the collective dynamics of large neural populations in terms of averaged macroscopic variables. Recently, the so-called next-generation neural mass models have attracted a lot… read more here.
Keywords: shot noise; neural mass; finite size; mass models ... See more keywords
Global nonlinear approach for mapping parameters of neural mass models
Sign Up to like & getrecommendations! Published in 2023 at "PLOS Computational Biology"
DOI: 10.1371/journal.pcbi.1010985
Abstract: Neural mass models (NMMs) are important for helping us interpret observations of brain dynamics. They provide a means to understand data in terms of mechanisms such as synaptic interactions between excitatory and inhibitory neuronal populations.… read more here.
Keywords: nonlinear approach; global nonlinear; neural mass; parameter ... See more keywords